Data Scientist - Tech Lead

About the role

We are seeking a Data Scientist – Tech Lead to lead our data science team, defining technical direction, modeling approaches, and quality standards while owning the full lifecycle of data science initiatives.

Key responsibilities

• Lead a team of data scientists, defining technical direction, modeling approaches, and quality standards.
• Own the full lifecycle of data science initiatives from problem framing to deployment and monitoring.
• Design and oversee experimentation frameworks, including A/B testing and causal inference.
• Define standards for reproducibility, code quality, model documentation, and performance monitoring.
• Decide between classical ML, statistical modeling, or LLM‑based approaches and justify trade‑offs.
• Partner with data engineering, product, and business stakeholders to secure data and translate results.
• Mentor the team through code reviews, technical guidance, and career development.

Required profile

• Minimum 6 years of experience as a Data Scientist, with at least 2 years leading or mentoring a team.
• Proven track record of taking models to production and maintaining them post‑deployment.
• Strong development skills in Python (pandas, scikit‑learn, PyTorch or TensorFlow) and SQL on large datasets.
• Solid statistical foundation in experimentation design, A/B testing, hypothesis testing, and causal inference.
• Experience with the full ML lifecycle: feature engineering, validation, monitoring, drift detection, and retraining.
• Hands‑on experience applying LLMs or GenAI to production use cases.
• Experience with distributed processing (Spark / Databricks or equivalent) and cloud environments (AWS, GCP or Azure).
• Ability to communicate methodology, results, and uncertainty to non‑technical stakeholders.
• Advanced English level.

Required skills

• Python (pandas, scikit‑learn, PyTorch, TensorFlow)
• SQL
• Spark / Databricks
• AWS, GCP, Azure
• MLflow (MLOps tooling)
• Feature stores
• Model registries

What we offer

• 20 paid days off per year.
• Hybrid work model.
• Flex Days: monthly team activities.
• Professional development courses.
• Internal clubs (soccer, padel, running, cinema).